Image skin segmentation based on multi-agent learning Bayesian and neural network - Aos Alaa, Zaidan; Ahmad, Nurul Nadia; Hezerul, Abdul Karim; M. Larbani, Moussa; Bilal Bahaa, Zaidan; Aduwati, Sali (Elsevier, 2014)
Skin colour is considered to be a useful and discriminating spatial feature for many skin detectionrelated applications, but it is not sufﬁciently robust to address complex image environments because of light-changing conditions, skin-like colours and reﬂective glass or water. These factors can create major difﬁculties in face pixel-based skin detectors when the colour feature is used. Thus, this paper proposes a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces, respectively, to improve skin detection performance. The ﬁndings from this study have shown that the proposed multi-agent learning for skin detector has produced signiﬁcant true positive (TP) and true negative (TN) average rates (i.e. 98.44% and 99.86% respectively). In addition, it has achieved a signiﬁcantlylower averagerate for the false negative (FN) and false positive (FP) (i.e. only 1.56% and 0.14% respectively). The experimental results show that multiagent learning in the skin detector is more efﬁcient than other approaches. & 2014 Elsevier Ltd. All rights reserved